During perceptual decisions, the activity of sensory neurons correlates with a subjects percept, even when the physical stimulus is identical1. The origin of this correlation is unknown. Current theory proposes a causal effect of noise in sensory neurons on perceptual decisions, but it could result from different brain-states associated with the perceptual choice (top-down). These two schemes have very different implications for the role played by sensory neurons in forming decisions. Here, we used white-noise analysis to measure tuning-functions of V2 neurons associated with choice and simultaneously measure how the variation in the stimulus affects subjects (two macaques) perceptual decisions. In causal models stronger effects of the stimulus upon decisions, mediated by sensory neurons, are associated with stronger choice-related activity. However, we find that over the timecourse of the trial, these measures change in opposite directionsat odds with causal models. An analysis of effect of reward size supports the same conclusion. Finally, choice was associated with changes in neuronal gain that are incompatible with causal models. All three results are readily explained if choice is associated with changes in neuronal gain caused by top-down phenomena that closely resemble attention. We conclude that top-down processes contribute to choice-related activity. Thus even forming simple sensory decisions involves complex interactions between cognitive processes and sensory neurons. Considerable progress has been made towards explaining the neuronal mechanisms underlying decision making12- a major goal in systems neuroscience. For simple perceptual decisions, recent theory proposes that sensorimotor areas accumulate sensory evidence about the physical world, delivered by sensory neurons. Noise in the sensory neurons causes variability in the behavioral response, resulting in a co-variation between the neuronal activity and behavior. (Note that this causal effect of noise in the sensory representation has only been invoked for sensory areas, not for sensorimotor areas.) However, this co-variation could also arise from top-down effects in which brain statesthat are associated with one behavioral response, also alter the response of the sensory neurons. A third (bottom-up) possibility is that sensory neurons that themselves have no causal effect on the decision are correlated with sensory neurons that do have a causal effect. These schemes have markedly different implications for the role played by sensory neurons in forming decisions. Sensory neurons either only encode the physical stimulus, or they simultaneously form an integral part of the mechanism used by the brain to decode the sensory information. In order to distinguish these views, we combined the measurement of choice-related activity in disparity selective V2 neurons in a disparity discrimination task, with a stimulus that permitted the use of white-noise analysis. This allowed the simultaneous application of 1) subspace mapping, to describe how disparity affects the neuronal response (disparity subspace map), and 2) psychophysical reverse correlation to extract a kernel describing how disparity affects the subjects (two macaques) perceptual choices. This comprehensive dataset allowed us to show that the bottom up scheme is cannot explain these phenomena.